{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Batch Normalization Keras\n",
    "Normalize each batch by both mean and variance reference\n",
    "### Purpose\n",
    "As the data flows through a deep network, the weights and parameters adjust those values, sometimes making the data too big or too small again - a problem the authors refer to as \"internal covariate shift\". By normalizing the data in each mini-batch, this problem is largely avoided.\n",
    "\n",
    "### Benefits:\n",
    "- Networks train faster converge much more quickly,\n",
    "- Allows higher learning rates Gradient descent usually requires small learning rates for the network to converge.\n",
    "- Makes weights easier to initialize\n",
    "- Makes more activation functions viable Because batch normalization regulates the values going into each activation function, non-linearlities that don't seem to work well in deep networks actually become viable again.\n",
    "- May give better results overall  it's really an optimization to help train faster, so you shouldn't think of it as a way to make your network better.\n",
    "\n",
    "### Keypoints\n",
    "- Batch normalization uses weights as usual, but does NOT add a bias term. This is because, its calculations include gamma and beta variables that make the bias term unnecessary. In Keras `Dense(64, use_bias=False)` or `Conv2D(32, (3, 3), use_bias=False)`\n",
    "- We add the normalization before calling the activation function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.0.8-tf'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow.python import keras\n",
    "from tensorflow.python.keras import models\n",
    "from tensorflow.python.keras import layers\n",
    "from tensorflow.python.keras.callbacks import TensorBoard\n",
    "from tensorflow.python.keras.datasets import mnist\n",
    "from tensorflow.python.keras.utils import to_categorical\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "keras.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train_images, train_labels), (test_images, test_labels) = mnist.load_data()\n",
    "\n",
    "train_images = train_images.reshape((60000, 28, 28, 1))\n",
    "train_images = train_images.astype('float32') / 255\n",
    "\n",
    "test_images = test_images.reshape((10000, 28, 28, 1))\n",
    "test_images = test_images.astype('float32') / 255\n",
    "\n",
    "train_labels = to_categorical(train_labels)  # one-hot\n",
    "test_labels = to_categorical(test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_generator(x, y, batch_size=32):\n",
    "    batches = int(len(x)/batch_size)\n",
    "    while 1:\n",
    "        for i in range(batches):\n",
    "            yield x[i*batch_size:(i+1)*batch_size], y[i*batch_size:(i+1)*batch_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "img_size=(28, 28, 1)\n",
    "class NeuralNet:\n",
    "    def __init__(self, use_batch_norm, activation='relu'):\n",
    "        self.use_batch_norm = use_batch_norm\n",
    "        self.build_model(activation=activation)\n",
    "    def add_dense_layer(self, units, activation='relu'):\n",
    "        if self.use_batch_norm:\n",
    "            self.model.add(layers.Dense(units, use_bias=False))\n",
    "            self.model.add(layers.BatchNormalization())\n",
    "            self.model.add(layers.Activation(activation))\n",
    "        else:\n",
    "            self.model.add(layers.Dense(units, activation=activation))\n",
    "    def add_conv2d_layer(self, filters, kernel_size, activation='relu', **kwargs):\n",
    "        if self.use_batch_norm:\n",
    "            self.model.add(layers.Conv2D(filters, kernel_size, use_bias=False, **kwargs))\n",
    "            self.model.add(layers.BatchNormalization())\n",
    "            self.model.add(layers.Activation(activation))\n",
    "        else:\n",
    "            self.model.add(layers.Conv2D(filters, kernel_size, activation=activation, **kwargs))\n",
    "    def build_model(self, activation):\n",
    "        self.model = models.Sequential()\n",
    "        self.add_conv2d_layer(32, (3, 3), activation=activation, input_shape=img_size)\n",
    "        self.model.add(layers.MaxPooling2D((2, 2)))\n",
    "        self.add_conv2d_layer(64, (3, 3), activation=activation)\n",
    "        self.model.add(layers.MaxPooling2D((2, 2)))\n",
    "        self.add_conv2d_layer(64, (3, 3), activation=activation)\n",
    "        self.model.add(layers.Flatten())\n",
    "        self.add_dense_layer(64, activation=activation)\n",
    "        self.add_dense_layer(10, activation='softmax')\n",
    "    def train(self, learning_rate=0.001, epoches=40, batch_size=32, steps_per_epoch=30):\n",
    "        self.model.compile(loss='categorical_crossentropy',\n",
    "              optimizer=tf.keras.optimizers.Adam(lr=learning_rate),\n",
    "              metrics=['accuracy'])\n",
    "        history = self.model.fit_generator(generator=data_generator(train_images, train_labels, batch_size=batch_size),\n",
    "                                      steps_per_epoch = steps_per_epoch,\n",
    "                                      epochs = epoches,\n",
    "                                      validation_data=data_generator(test_images, test_labels, batch_size=batch_size),\n",
    "                                      validation_steps=len(test_images)/batch_size)\n",
    "        return history.history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_training_accuracies(*args, **kwargs):\n",
    "    \"\"\"\n",
    "    Displays a plot of the accuracies calculated during training to demonstrate\n",
    "    how many iterations it took for the model(s) to converge.\n",
    "    \n",
    "    :param args: One or more NeuralNet objects\n",
    "        You can supply any number of NeuralNet objects as unnamed arguments \n",
    "        and this will display their training accuracies. Be sure to call `train` \n",
    "        the NeuralNets before calling this function.\n",
    "    :param kwargs: \n",
    "        You can supply any named parameters here, but `steps_per_epoch` is the only\n",
    "        one we look for. It should match the `steps_per_epoch` value you passed\n",
    "        to the `train` function.\n",
    "    \"\"\"\n",
    "    fig, ax = plt.subplots()\n",
    "\n",
    "    steps_per_epoch = kwargs['steps_per_epoch']\n",
    "    \n",
    "    for history in args:\n",
    "        ax.plot(range(0,len(history['acc'])*steps_per_epoch,steps_per_epoch),\n",
    "                history['acc'], label=\"acc-\" + history['name'])\n",
    "        ax.plot(range(0,len(history['val_acc'])*steps_per_epoch,steps_per_epoch),\n",
    "                history['val_acc'], label=\"val_acc-\"+history['name'])\n",
    "    ax.set_xlabel('Training steps')\n",
    "    ax.set_ylabel('Accuracy')\n",
    "    ax.set_title('Accuracy During Training')\n",
    "    ax.legend(loc=4)\n",
    "    #ax.set_ylim([0,1])\n",
    "    #plt.yticks(np.arange(0, 1.1, 0.1))\n",
    "    plt.grid(True)\n",
    "    plt.show()\n",
    "\n",
    "    \n",
    "def train_and_test(learning_rate=0.001, activation=\"relu\", epochs=40, steps_per_epoch=30):\n",
    "    nn = NeuralNet(use_batch_norm=False, activation=activation)\n",
    "    bn = NeuralNet(use_batch_norm=True, activation=activation)\n",
    "    history_nn = nn.train(learning_rate=learning_rate, epoches=epochs, steps_per_epoch=steps_per_epoch)\n",
    "    history_bn = bn.train(learning_rate=learning_rate, epoches=epochs, steps_per_epoch=steps_per_epoch)\n",
    "    history_nn['name'] = \"Without batch normalization\"\n",
    "    history_bn['name'] = \"With batch normalization\"\n",
    "    plot_training_accuracies(history_nn, history_bn, steps_per_epoch=steps_per_epoch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 16s - loss: 0.1466 - acc: 0.9556 - val_loss: 0.0641 - val_acc: 0.9799\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 13s - loss: 0.0456 - acc: 0.9864 - val_loss: 0.0384 - val_acc: 0.9874\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 14s - loss: 0.0320 - acc: 0.9902 - val_loss: 0.0308 - val_acc: 0.9893\n",
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.2696 - acc: 0.9621 - val_loss: 0.0781 - val_acc: 0.9874\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 18s - loss: 0.0892 - acc: 0.9839 - val_loss: 0.0422 - val_acc: 0.9904\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.0497 - acc: 0.9907 - val_loss: 0.0363 - val_acc: 0.9899\n"
     ]
    },
    {
     "data": {
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unBL25FS+t8XCJkCgc4wQG3uAXn13USjbyC7fieImKCCIwXGDSU6YSHJCMoM7DCZMgqqF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\nfenQ4do7Sk8++WQhnUuWLGHWrFl4enoSGBjIyZM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YY48VshcYGEhkZCRjxowhJiaGUaNG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\nmzenaVNLfJnBgwfz8MMP4+fnh7e3Nx06dCiTLWv53NzceP311+nZsyc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d07c649da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_test(learning_rate=0.001, activation='relu', epochs=3, steps_per_epoch=1875)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using a sigmoid activation function takes a long time to start learning. It eventually starts making progress, but it took over 250 batches just to get over 70% accuracy. Using batch normalization gets to 90% in around 100 batches."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 14s - loss: 0.6409 - acc: 0.7923 - val_loss: 0.1481 - val_acc: 0.9563\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 14s - loss: 0.1160 - acc: 0.9659 - val_loss: 0.0870 - val_acc: 0.9737\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 13s - loss: 0.0753 - acc: 0.9772 - val_loss: 0.0812 - val_acc: 0.9755\n",
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.3273 - acc: 0.9506 - val_loss: 0.3006 - val_acc: 0.9298\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 18s - loss: 0.1341 - acc: 0.9731 - val_loss: 0.2695 - val_acc: 0.9211\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.0864 - acc: 0.9813 - val_loss: 0.2049 - val_acc: 0.9364\n"
     ]
    },
    {
     "data": {
      "image/png": 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uPMWM0DpOUY/H70U0SlofPOQXD19FRaO2jsRESxTS0okaNqyRBxGRloYzJQVx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\n9l7jqlWrWLx4MR9++CFgeXyPP/44GzZ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4+fmRm5tLq1ataNq0KXFxcRw6dAhfX1+7DWe2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\n8f9G0dkyfgg+eAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0506aa588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_test(learning_rate=0.001, activation='sigmoid', epochs=3, steps_per_epoch=1875)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 15s - loss: 0.1663 - acc: 0.9516 - val_loss: 0.1294 - val_acc: 0.9653\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 14s - loss: 0.1118 - acc: 0.9698 - val_loss: 0.1297 - val_acc: 0.9663\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 14s - loss: 0.1092 - acc: 0.9725 - val_loss: 0.1073 - val_acc: 0.9749\n",
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 20s - loss: 0.1427 - acc: 0.9649 - val_loss: 0.0449 - val_acc: 0.9861\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.0572 - acc: 0.9843 - val_loss: 0.0346 - val_acc: 0.9892\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 18s - loss: 0.0405 - acc: 0.9889 - val_loss: 0.0322 - val_acc: 0.9900\n"
     ]
    },
    {
     "data": {
      "image/png": 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URrQd4ZmKSo1O9flOqUDCCMlphrrduA4frsEnouLedeTIMWUlIsKaWkpIpFnv3hWjB3sk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\nW+c47tq1az7tS5YsISkpieHDhxMSEkJmZiYA77/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aNWnevHm587usIRERT2AW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d092ceef28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_test(learning_rate=0.01, activation='relu', epochs=3, steps_per_epoch=1875)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 15s - loss: 2.3207 - acc: 0.1000 - val_loss: 2.3055 - val_acc: 0.1136\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 13s - loss: 2.3196 - acc: 0.1006 - val_loss: 2.3056 - val_acc: 0.1138\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 14s - loss: 2.3196 - acc: 0.1007 - val_loss: 2.3056 - val_acc: 0.1135\n",
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.1780 - acc: 0.9555 - val_loss: 0.1830 - val_acc: 0.9371\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.0698 - acc: 0.9802 - val_loss: 0.1817 - val_acc: 0.9388\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.0471 - acc: 0.9865 - val_loss: 0.1507 - val_acc: 0.9513\n"
     ]
    },
    {
     "data": {
      "image/png": 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yK66RI0cCnnsQO3fuBGDZsmVu/csuu4xWrVrx73//u2o7DoSEhHD77bcDcNdd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\nIRT8AkOrkbzXZIaxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d098fc6da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_test(learning_rate=0.01, activation='sigmoid', epochs=3, steps_per_epoch=1875)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 16s - loss: 2.3197 - acc: 0.1052 - val_loss: 2.3031 - val_acc: 0.1028\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 14s - loss: 2.3060 - acc: 0.1046 - val_loss: 2.3031 - val_acc: 0.1027\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 14s - loss: 2.3060 - acc: 0.1045 - val_loss: 2.3031 - val_acc: 0.1028\n",
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 20s - loss: 0.1423 - acc: 0.9584 - val_loss: 0.0635 - val_acc: 0.9808\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.0664 - acc: 0.9805 - val_loss: 0.0556 - val_acc: 0.9846\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.0512 - acc: 0.9846 - val_loss: 0.0918 - val_acc: 0.9725\n"
     ]
    },
    {
     "data": {
      "image/png": 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lww8/BGDbtm38+OOP7Nu3j379+nnf6z777LMBWLhwIcFjdzRs2LBYfS1btiQv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d09d17fa58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_test(learning_rate=0.05, activation='relu', epochs=3, steps_per_epoch=1875)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 16s - loss: 2.4200 - acc: 0.0968 - val_loss: 2.3309 - val_acc: 0.0957\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 15s - loss: 2.4183 - acc: 0.0970 - val_loss: 2.3311 - val_acc: 0.0958\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 14s - loss: 2.4183 - acc: 0.0970 - val_loss: 2.3312 - val_acc: 0.0956\n",
      "Epoch 1/3\n",
      "1875/1875 [==============================] - 21s - loss: 0.1550 - acc: 0.9546 - val_loss: 0.3872 - val_acc: 0.8604\n",
      "Epoch 2/3\n",
      "1875/1875 [==============================] - 18s - loss: 0.0713 - acc: 0.9784 - val_loss: 0.1212 - val_acc: 0.9615\n",
      "Epoch 3/3\n",
      "1875/1875 [==============================] - 19s - loss: 0.0562 - acc: 0.9834 - val_loss: 0.6120 - val_acc: 0.8367\n"
     ]
    },
    {
     "data": {
      "image/png": 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UecqVC80LgZXGmA8AA0xBO8RTSqlfJVcuNL9gjNkKXIG9D6RvgDB3B6aUUqrx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\ngTrF99xzz9W6jPO4B57MeQyL0aNHk52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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0a08e7e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_test(learning_rate=0.05, activation='sigmoid', epochs=3, steps_per_epoch=1875)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Try a smaller steps per epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "187/187 [==============================] - 3s - loss: 0.5941 - acc: 0.8234 - val_loss: 0.2623 - val_acc: 0.9182\n",
      "Epoch 2/10\n",
      "187/187 [==============================] - 2s - loss: 0.2010 - acc: 0.9408 - val_loss: 0.1624 - val_acc: 0.9502\n",
      "Epoch 3/10\n",
      "187/187 [==============================] - 2s - loss: 0.1423 - acc: 0.9552 - val_loss: 0.1097 - val_acc: 0.9645\n",
      "Epoch 4/10\n",
      "187/187 [==============================] - 2s - loss: 0.0973 - acc: 0.9711 - val_loss: 0.0728 - val_acc: 0.9782\n",
      "Epoch 5/10\n",
      "187/187 [==============================] - 2s - loss: 0.0883 - acc: 0.9721 - val_loss: 0.0773 - val_acc: 0.9757\n",
      "Epoch 6/10\n",
      "187/187 [==============================] - 2s - loss: 0.0845 - acc: 0.9749 - val_loss: 0.0581 - val_acc: 0.9824\n",
      "Epoch 7/10\n",
      "187/187 [==============================] - 2s - loss: 0.0771 - acc: 0.9766 - val_loss: 0.0577 - val_acc: 0.9804\n",
      "Epoch 8/10\n",
      "187/187 [==============================] - 2s - loss: 0.0825 - acc: 0.9741 - val_loss: 0.0535 - val_acc: 0.9832\n",
      "Epoch 9/10\n",
      "187/187 [==============================] - 2s - loss: 0.0749 - acc: 0.9774 - val_loss: 0.0444 - val_acc: 0.9852\n",
      "Epoch 10/10\n",
      "187/187 [==============================] - 2s - loss: 0.0518 - acc: 0.9846 - val_loss: 0.0467 - val_acc: 0.9852\n",
      "Epoch 1/10\n",
      "187/187 [==============================] - 4s - loss: 0.6775 - acc: 0.8852 - val_loss: 2.3764 - val_acc: 0.1995\n",
      "Epoch 2/10\n",
      "187/187 [==============================] - 3s - loss: 0.4181 - acc: 0.9484 - val_loss: 1.7719 - val_acc: 0.3171\n",
      "Epoch 3/10\n",
      "187/187 [==============================] - 2s - loss: 0.3367 - acc: 0.9591 - val_loss: 0.3564 - val_acc: 0.9568\n",
      "Epoch 4/10\n",
      "187/187 [==============================] - 2s - loss: 0.2564 - acc: 0.9703 - val_loss: 0.1796 - val_acc: 0.9788\n",
      "Epoch 5/10\n",
      "187/187 [==============================] - 2s - loss: 0.2259 - acc: 0.9719 - val_loss: 0.2048 - val_acc: 0.9680\n",
      "Epoch 6/10\n",
      "187/187 [==============================] - 2s - loss: 0.2106 - acc: 0.9686 - val_loss: 0.1056 - val_acc: 0.9883\n",
      "Epoch 7/10\n",
      "187/187 [==============================] - 2s - loss: 0.1798 - acc: 0.9754 - val_loss: 0.1023 - val_acc: 0.9869\n",
      "Epoch 8/10\n",
      "187/187 [==============================] - 2s - loss: 0.1585 - acc: 0.9746 - val_loss: 0.0916 - val_acc: 0.9860\n",
      "Epoch 9/10\n",
      "187/187 [==============================] - 2s - loss: 0.1518 - acc: 0.9734 - val_loss: 0.1127 - val_acc: 0.9839\n",
      "Epoch 10/10\n",
      "187/187 [==============================] - 2s - loss: 0.1241 - acc: 0.9808 - val_loss: 0.0685 - val_acc: 0.9877\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d09f0015f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DiU0c2HDgEgdp4sQuDuziwGFLwi4OnDYnDpsz8urAaUvCLjYC6sUf9uAPH8EX8uAPe/CG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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0a7f06cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_and_test(learning_rate=0.001, activation='relu', epochs=10, steps_per_epoch=int(1875/10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
